Spaces:
Sleeping
Sleeping
| import streamlit as st | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| from sentence_transformers import SentenceTransformer, util | |
| import PyPDF2 | |
| from docx import Document | |
| from nltk.corpus import wordnet as wn | |
| import nltk | |
| import pandas as pd | |
| # Ensure required resources are downloaded | |
| nltk.download('wordnet') | |
| nltk.download('omw-1.4') | |
| # Load the tokenizer and model for sentence embeddings | |
| def load_model(): | |
| try: | |
| tokenizer = AutoTokenizer.from_pretrained("rakeshkiriyath/gpt2Medium_text_to_sql") | |
| model = AutoModelForCausalLM.from_pretrained("rakeshkiriyath/gpt2Medium_text_to_sql") | |
| sentence_model = SentenceTransformer('all-MiniLM-L6-v2') # Smaller, faster sentence embeddings model | |
| st.success("Model loaded successfully!") | |
| return tokenizer, model, sentence_model | |
| except Exception as e: | |
| st.error(f"Error loading models: {e}") | |
| return None, None, None | |
| # Extract text from a PDF file | |
| def extract_text_from_pdf(pdf_file): | |
| try: | |
| pdf_reader = PyPDF2.PdfReader(pdf_file) | |
| text = "" | |
| for page in pdf_reader.pages: | |
| text += page.extract_text() | |
| return text | |
| except Exception as e: | |
| st.error(f"Error reading PDF: {e}") | |
| return "" | |
| # Extract text from a Word document | |
| def extract_text_from_word(docx_file): | |
| try: | |
| doc = Document(docx_file) | |
| text = "" | |
| for paragraph in doc.paragraphs: | |
| text += paragraph.text + "\n" | |
| return text | |
| except Exception as e: | |
| st.error(f"Error reading Word document: {e}") | |
| return "" | |
| # Optimized comparison using embeddings and matrix operations | |
| def compare_sentences(doc1_sentences, doc2_sentences, sentence_model): | |
| # Encode all sentences in batches to get embeddings | |
| doc1_embeddings = sentence_model.encode(doc1_sentences, convert_to_tensor=True, batch_size=16) | |
| doc2_embeddings = sentence_model.encode(doc2_sentences, convert_to_tensor=True, batch_size=16) | |
| # Compute cosine similarity matrix between all pairs | |
| similarity_matrix = util.pytorch_cos_sim(doc1_embeddings, doc2_embeddings) | |
| # Extract pairs with similarity > threshold | |
| threshold = 0.6 # Adjust this for stricter or looser matching | |
| similar_sentences = [] | |
| for i, row in enumerate(similarity_matrix): | |
| for j, score in enumerate(row): | |
| if score >= threshold: | |
| similar_sentences.append((i, j, score.item(), doc1_sentences[i], doc2_sentences[j])) | |
| return similar_sentences | |
| # Find similar words or synonyms between two sentences | |
| def find_similar_words(sentence1, sentence2): | |
| words1 = set(sentence1.split()) | |
| words2 = set(sentence2.split()) | |
| similar_words = [] | |
| for word1 in words1: | |
| for word2 in words2: | |
| if word1 == word2 or is_synonym(word1, word2): | |
| similar_words.append((word1, word2)) | |
| return similar_words | |
| # Check if two words are synonyms using WordNet | |
| def is_synonym(word1, word2): | |
| synonyms_word1 = set(lemma.name() for synset in wn.synsets(word1) for lemma in synset.lemmas()) | |
| synonyms_word2 = set(lemma.name() for synset in wn.synsets(word2) for lemma in synset.lemmas()) | |
| return len(synonyms_word1.intersection(synonyms_word2)) > 0 | |
| # Streamlit UI | |
| def main(): | |
| st.title("Enhanced Comparative Analysis of Two Documents") | |
| st.sidebar.header("Upload Files") | |
| # Upload files | |
| uploaded_file1 = st.sidebar.file_uploader("Upload the First Document (PDF/Word)", type=["pdf", "docx"]) | |
| uploaded_file2 = st.sidebar.file_uploader("Upload the Second Document (PDF/Word)", type=["pdf", "docx"]) | |
| if uploaded_file1 and uploaded_file2: | |
| # Extract text from the uploaded documents | |
| if uploaded_file1.name.endswith(".pdf"): | |
| text1 = extract_text_from_pdf(uploaded_file1) | |
| else: | |
| text1 = extract_text_from_word(uploaded_file1) | |
| if uploaded_file2.name.endswith(".pdf"): | |
| text2 = extract_text_from_pdf(uploaded_file2) | |
| else: | |
| text2 = extract_text_from_word(uploaded_file2) | |
| if not text1.strip(): | |
| st.error("The first document is empty or could not be read.") | |
| return | |
| if not text2.strip(): | |
| st.error("The second document is empty or could not be read.") | |
| return | |
| st.write("### Preview of Document 1:") | |
| st.text(text1[:500]) # Display a preview of Document 1 | |
| st.write("### Preview of Document 2:") | |
| st.text(text2[:500]) # Display a preview of Document 2 | |
| # Split text into sentences | |
| doc1_sentences = text1.split('. ') | |
| doc2_sentences = text2.split('. ') | |
| # Limit sentences for testing purposes (optional) | |
| doc1_sentences = doc1_sentences[:50] # Remove this line for full processing | |
| doc2_sentences = doc2_sentences[:50] # Remove this line for full processing | |
| # Load models | |
| tokenizer, model, sentence_model = load_model() | |
| if not sentence_model: | |
| st.error("Failed to load the sentence embedding model.") | |
| return | |
| # Perform sentence comparison | |
| st.info("Comparing sentences, this may take a moment...") | |
| similar_sentences = compare_sentences(doc1_sentences, doc2_sentences, sentence_model) | |
| # Display results | |
| st.header("Comparative Analysis Results") | |
| st.write(f"Number of sentences in Document 1: {len(doc1_sentences)}") | |
| st.write(f"Number of sentences in Document 2: {len(doc2_sentences)}") | |
| if similar_sentences: | |
| st.success(f"Found {len(similar_sentences)} similar sentences!") | |
| # Prepare table for similar words | |
| table_data = [] | |
| for match in similar_sentences: | |
| doc1_index, doc2_index, score, sent1, sent2 = match | |
| similar_words = find_similar_words(sent1, sent2) | |
| similar_words_str = ", ".join([f"({w1}, {w2})" for w1, w2 in similar_words]) | |
| table_data.append([f"Sentence {doc1_index + 1}", f"Sentence {doc2_index + 1}", score, similar_words_str]) | |
| # Create a DataFrame for display | |
| comparison_df = pd.DataFrame(table_data, columns=["Document 1 Sentence", "Document 2 Sentence", "Similarity Score", "Similar Words/Synonyms"]) | |
| st.table(comparison_df) | |
| else: | |
| st.info("No significantly similar sentences found.") | |
| else: | |
| st.warning("Please upload two documents to compare.") | |
| if __name__ == "__main__": | |
| main() | |